A wide range of imaging methods and devices are commonly used to evaluate different anatomical and physiological conditions in a variety of medical and research environments. Tools have been developed to image body structures based on different physical properties. For example, X-rays, CT scans, MRIs, PET scans, IR analyses and other technologies have been developed to obtain images of various body structures. These tools are routinely used for diagnostic, therapeutic, and research applications. Combinations of two or more different imaging techniques are sometimes used to provide complementary information about a patient.
In conventional medical imaging, a human operator, such as a physician or diagnostician, may visually inspect one or more images to make an assessment, such as detection of a tumor or other pathology or to otherwise characterize the internal structures of a patient. However, this process may be difficult and time consuming. For example, it may be difficult to assess 3D biological structure by attempting to follow 2D structure through a series of stacked 2D images. In particular, it may be perceptually difficult and time consuming to understand how 2D structure is related to 3D structure as it appears, changes in size and shape, and/or disappears in successive 2D image slices. A physician may have to mentally arrange hundreds or more 2D slices into a 3D picture of the anatomy. To further frustrate this process, when anatomical structure of interest is small, the structure may be difficult to discern or it may be difficult to understand how numerous structures relate to a biological whole.
Furthermore, in addition to the time consuming nature of manual inspection, human visual interpretation of images has further shortcomings. While the human visual cortex processes image information to obtain qualitative information about structure in the image, it does not compute quantitative geometry from the image. However, the quantitative geometry of the structure represented in one or more images may contain valuable information about the structure that can be used to diagnose disease, assess the efficacy of treatment and/or perform other analyses of the structure. Such quantitative information about the structure is beyond the capability of conventional human visual image understanding alone.
Image processing techniques have been developed to automate or partially automate the task of understanding and partitioning the structure in an image and are employed in computer aided diagnosis (CAD) to assist a physician in identifying and locating structure of interest in a 2D or 3D image. CAD techniques often involve segmenting the image into groups of related pixels and identifying the various groups of pixels, for example, as those comprising a tumor or a vessel or some other structure of interest. However, conventional segmentation may produce unsatisfactory or incomplete results, particularly when the structure being detected appears in the image at arbitrary locations, sizes and orientations. As a result, the limited geometry that may be extracted from conventional image processing may be unsuitable for use in further analysis based on the extracted geometry.